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146 lines
3.7 KiB
146 lines
3.7 KiB
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "paddle/utils/Util.h"
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#include <stdio.h>
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#include <fstream>
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#include <stdlib.h>
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#include "hl_gpu.h"
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#include "paddle/gserver/gradientmachines/GradientMachine.h"
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#include "TrainerConfig.pb.h"
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#include "ParameterUpdater.h"
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#include "TrainerConfigHelper.h"
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#include "TrainerInternalConfig.h"
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namespace paddle {
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/**
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* TrainerInteral
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* the core training class for driving training logic
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*/
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class TrainerInternal {
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public:
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struct ParaStat {
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real maxAbsGrad;
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real avgAbsGrad;
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ParaStat() :maxAbsGrad(.0), avgAbsGrad(.0){
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}
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};
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TrainerInternal() {
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}
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/**
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* Intializes trainer internal class
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* @param config network config
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* @param machine gradient machine
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* @param intconfig training config
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* @param stats training stats
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* @param testing if it is in testing phase
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*/
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void init(const std::shared_ptr<TrainerConfigHelper> &config,
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const GradientMachinePtr &machine,
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std::unique_ptr<TrainerInternalConfig> &&intconfig,
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const std::shared_ptr<TrainerStats> &stats,
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bool testing);
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virtual ~TrainerInternal() {}
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/**
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* CreateParameterUpdater
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* @param testing if it is in testing phase
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*/
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void createParameterUpdater(bool testing);
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/**
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* FinishTrainPass
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* @param passId current pass id
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* @param batchId current batch id, starts from 0
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*/
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void finishTrainPass(int passId, int batchId);
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/**
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* trainOneBatch
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* @param batchId current batch id
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* @param dataBatch data for the batch
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*/
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void trainOneBatch(int64_t batchId, const DataBatch& dataBatch);
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/**
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* showParameterStats
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* @param paraStats training stats
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*/
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void showParameterStats(const std::vector<ParaStat>& paraStats);
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/**
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* getGradientMachine
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*/
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inline const GradientMachinePtr & getGradientMachine() const {
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return gradientMachine_;
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}
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/**
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* getParameterUpdater
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*/
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inline const std::shared_ptr<ParameterUpdater>& getParameterUpdater() {
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return parameterUpdater_;
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}
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/**
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* setCurrentEvaluator
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* @param eval evaluator to set
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*/
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inline void setCurrentEvaluator(Evaluator* eval) {
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currentEvaluator_ = eval;
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}
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/**
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* setEvaluator
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* @param eval evaluator to set
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*/
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inline void setEvaluator(Evaluator* eval) {
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evaluator_ = eval;
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}
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/**
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* forwardBackwardBatch
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* @param inArgs input argument for data batch
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* @param outArgs output argument from neural network
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* @param updateCallback layerwise parameter gradient statistics
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* @param doPipelineUpdate whether to do pipeline update
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*/
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virtual void forwardBackwardBatch(const std::vector<Argument>& inArgs,
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std::vector<Argument>& outArgs,
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PassType& passType,
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UpdateCallback updateCallback,
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bool doPipelineUpdate);
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protected:
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std::shared_ptr<ParameterUpdater> parameterUpdater_;
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GradientMachinePtr gradientMachine_;
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std::shared_ptr<TrainerConfigHelper> config_;
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std::unique_ptr<TrainerInternalConfig> intconfig_;
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std::shared_ptr<TrainerStats> stats_;
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Evaluator* currentEvaluator_;
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Evaluator* evaluator_;
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};
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} // namespace paddle
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